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Linkage of survey data with district-level lung cancer registrations:a method of bias reduction in ecological studies

Lancaster, Gillian A and Green, Mick and Lane, Steven (2006) Linkage of survey data with district-level lung cancer registrations:a method of bias reduction in ecological studies. Journal of Epidemiology and Community Health, 60 (12). pp. 1093-1098. ISSN 0143-005X

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Abstract

Objective: To investigate a stratified ecological method for reducing ecological bias in studies that use aggregate data, by incorporating information on individual-level risk factors into the analysis. Design: Cross-sectional study investigating associations between socioeconomic risk factors and lung cancer in the north of England, using 1991 UK Census Small Area Statistics and Sample of Anonymised Records with lung cancer registrations from three regional cancer registries for 1993–6. Setting and patients: 92 local authority districts in the north of England containing over three million people aged 45–74 years. Results: Generally, groups considered more socioeconomically disadvantaged had an increased risk of lung cancer across districts. In the standard ecological analysis, effects for non-car ownership, social class III manual, social class IV/V and socioeconomic inactivity were insignificant, suggesting ecological bias. In the stratified ecological analysis these effects became significant (rate ratio (RR) 2.23, 95% confidence interval (CI) 1.79 to 2.78, p<0.001; RR 1.35, 95% CI 1.04 to 1.74, p = 0.022; RR 2.36, 95% CI 1.86 to 2.99, p<0.001; and RR 0.72, 95% CI 0.53 to 0.98, p = 0.039, respectively), and spuriously large positive effects for the social class III non-manual (RR 20.29) and unemployment groups (RR 147.53) reduced to a more reasonable level (RR 1.92, 95% CI 1.46 to 2.52, p<0.001; and RR 2.36, 95% CI 1.22 to 4.55, p = 0.011, respectively). Conclusions: Stratified ecological analysis incorporating information on individual-level covariates reduced the bias seen in a standard ecological analysis. The method is straightforward to apply and allows the linkage of health data with data from any large-scale complex survey where district of residence is known.

Item Type: Article
Journal or Publication Title: Journal of Epidemiology and Community Health
Subjects: Q Science > QA Mathematics
Departments: Faculty of Science and Technology > Mathematics and Statistics
ID Code: 60026
Deposited By: ep_importer_pure
Deposited On: 19 Nov 2012 10:01
Refereed?: Yes
Published?: Published
Last Modified: 10 Apr 2014 00:20
Identification Number:
URI: http://eprints.lancs.ac.uk/id/eprint/60026

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